Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.

Statistical analysis of personal radiofrequency electromagnetic field measurements with nondetects

Exposimeters are increasingly applied in bioelectromagnetic research to determine personal radiofrequency electromagnetic field (RF-EMF) exposure. The main advantages of exposimeter measurements are their convenient handling for study participants and the large amount of personal exposure data, which can be obtained for several RF-EMF sources. However, the large proportion of measurements below the detection limit is a challenge for data analysis. With the robust ROS (regression on order statistics) method, summary statistics can be calculated by fitting an assumed distribution to the observed data. We used a preliminary sample of 109 weekly exposimeter measurements from the QUALIFEX study to compare summary statistics computed by robust ROS with a naïve approach, where values below the detection limit were replaced by the value of the detection limit. For the total RF-EMF exposure, differences between the naïve approach and the robust ROS were moderate for the 90th percentile and the arithmetic mean. However, exposure contributions from minor RF-EMF sources were considerably overestimated with the naïve approach. This results in an underestimation of the exposure range in the population, which may bias the evaluation of potential exposure-response associations. We conclude from our analyses that summary statistics of exposimeter data calculated by robust ROS are more reliable and more informative than estimates based on a naïve approach. Nevertheless, estimates of source-specific medians or even lower percentiles depend on the assumed data distribution and should be considered with caution.

Combined Ultrasmall Superparamagnetic Particles of Iron Oxide–Enhanced and Diffusion-weighted Magnetic Resonance Imaging Facilitates Detection of Metastases in Normal-sized Pelvic Lymph Nodes of Patients with Bladder and Prostate Cancer

Background Conventional cross-sectional imaging with computed tomography and magnetic resonance imaging (MRI) has limited accuracy for lymph node (LN) staging in bladder and prostate cancer patients. Objective To prospectively assess the diagnostic accuracy of combined ultrasmall superparamagnetic particles of iron oxide (USPIO) MRI and diffusion-weighted (DW) MRI in staging of normal-sized pelvic LNs in bladder and/or prostate cancer patients. Design, setting, and participants Examinations with 3-Tesla MRI 24–36 h after administration of USPIO using conventional MRI sequences combined with DW-MRI (USPIO-DW-MRI) were performed in 75 patients with clinically localised bladder and/or prostate cancer staged previously as N0 by conventional cross-sectional imaging. Combined USPIO-DW-MRI findings were analysed by three independent readers and correlated with histopathologic LN findings after extended pelvic LN dissection (PLND) and resection of primary tumours. Outcome measurements and statistical analysis Sensitivity and specificity for LN status of combined USPIO-DW-MRI versus histopathologic findings were evaluated per patient (primary end point) and per pelvic side (secondary end point). Time required for combined USPIO-DW-MRI reading was assessed. Results and limitations At histopathologic analysis, 2993 LNs (median: 39 LNs; range: 17–68 LNs per patient) with 54 LN metastases (1.8%) were found in 20 of 75 (27%) patients. Per-patient sensitivity and specificity for detection of LN metastases by the three readers ranged from 65% to 75% and 93% to 96%, respectively; sensitivity and specificity per pelvic side ranged from 58% to 67% and 94% to 97%, respectively. Median reading time for the combined USPIO-DW-MRI images was 9 min (range: 3–26 min). A potential limitation is the absence of a node-to-node correlation of combined USPIO-DW-MRI and histopathologic analysis. Conclusions Combined USPIO-DW-MRI improves detection of metastases in normal-sized pelvic LNs of bladder and/or prostate cancer patients in a short reading time.

Image-based vs. mesh-based statistical appearance models of the human femur: Implications for finite element simulations.

Statistical appearance models have recently been introduced in bone mechanics to investigate bone geometry and mechanical properties in population studies. The establishment of accurate anatomical correspondences is a critical aspect for the construction of reliable models. Depending on the representation of a bone as an image or a mesh, correspondences are detected using image registration or mesh morphing. The objective of this study was to compare image-based and mesh-based statistical appearance models of the femur for finite element (FE) simulations. To this aim, (i) we compared correspondence detection methods on bone surface and in bone volume; (ii) we created an image-based and a mesh-based statistical appearance models from 130 images, which we validated using compactness, representation and generalization, and we analyzed the FE results on 50 recreated bones vs. original bones; (iii) we created 1000 new instances, and we compared the quality of the FE meshes. Results showed that the image-based approach was more accurate in volume correspondence detection and quality of FE meshes, whereas the mesh-based approach was more accurate for surface correspondence detection and model compactness. Based on our results, we recommend the use of image-based statistical appearance models for FE simulations of the femur.

Metastases in normal-sized pelvic lymph nodes: detection with diffusion-weighted MR imaging

PURPOSE To prospectively assess the diagnostic performance of diffusion-weighted (DW) magnetic resonance (MR) imaging in the detection of pelvic lymph node metastases in patients with prostate and/or bladder cancer staged as N0 with preoperative cross-sectional imaging. MATERIALS AND METHODS This study was approved by an independent ethics committee. Written informed consent was obtained from all patients. Patients with no enlarged lymph nodes on preoperative cross-sectional images who were scheduled for radical resection of the primary tumor and extended pelvic lymph node dissection were enrolled. All patients were examined with a 3-T MR unit, and examinations included conventional and DW MR imaging of the entire pelvis. Image analysis was performed by three independent readers blinded to any clinical information. Metastases were diagnosed on the basis of high signal intensity on high b value DW MR images and morphologic features (shape, border). Histopathologic examination served as the standard of reference. Sensitivity and specificity were calculated, and bias-corrected 95% confidence intervals (CIs) were obtained with the bootstrap method. The Fleiss and Cohen κ and median test were applied for statistical analyses. RESULTS A total of 4846 lymph nodes were resected in 120 patients. Eighty-eight lymph node metastases were found in 33 of 120 patients (27.5%). Short-axis diameter of these metastases was less than or equal to 3 mm in 68, more than 3 mm to 5 mm in 13, more than 5 mm to 8 mm in five; and more than 8 mm in two. On a per-patient level, the three readers correctly detected metastases in 26 (79%; 95% CI: 64%, 91%), 21 (64%; 95% CI: 45%, 79%), and 25 (76%; 95% CI: 60%, 90%) of the 33 patients with metastases, with respective specificities of 85% (95% CI: 78%, 92%), 79% (95% CI: 70%, 88%), and 84% (95% CI: 76%, 92%). Analyzed according to hemipelvis, lymph node metastases were detected with histopathologic examination in 44 of 240 pelvic sides (18%); the three readers correctly detected these on DW MR images in 26 (59%; 95% CI: 45%, 73%), 19 (43%; 95% CI: 27%, 57%), and 28 (64%; 95% CI: 47%, 78%) of the 44 cases. CONCLUSION DW MR imaging enables noninvasive detection of small lymph node metastases in normal-sized nodes in a substantial percentage of patients with prostate and bladder cancer diagnosed as N0 with conventional cross-sectional imaging techniques.

Diagnostic Performance and Additional Value of Elastosonography in Focal Breast Lesions: Statistical Correlation between Size-Dependant Strain Index Measurements, Multimodality-BI-RADS Score, and Histopathology in a Clinical Routine Setting

Objective. To evaluate the diagnostic benefit of real-time elastography (RTE) in clinical routine. Strain indices (SI) for benign and malignant tumors were assessed. Methods. 100 patients with 110 focal breast lesions were retrieved. Patients had mammography (MG), ultrasound (US), and, if necessary, MRI. RTE was conducted after ultrasound. Lesions were assessed with BI-RADS for mammography and ultrasound. Diagnosis was established with histology or follow-up. Results. SI for BI-RADS 2 was 1.71 ± 0.86. Higher SI (2.21 ± 1.96) was observed for BI-RADS 3 lesions. SI of BI-RADS 4 and 5 lesions were significantly higher (16.92 ± 20.89) and (19.54 ± 10.41). 31 malignant tumors exhibited an average SI of 16.13 ± 14.67; SI of benign lesions was 5.29 ± 11.87 (P value <0.0001). ROC analysis threshold was >3.8 for malignant disease. Sensitivity of sonography was 90.3% (specificity 78.5%). RTE showed a sensitivity of 87.1% (specificity 79.7%). Accuracy of all modalities combined was 96.8%. In BI-RADS 3 lesions RTE was able to detect all malignant lesions (sensitivity 100%, specificity 92.9%, and accuracy 93.9%). Conclusions. RTE increased sensitivity and specificity for breast cancer detection when used in combination with ultrasound.

Streak detection algorithm for space debris detection on optical images.

Any image processing object detection algorithm somehow tries to integrate the object light (Recognition Step) and applies statistical criteria to distinguish objects of interest from other objects or from pure background (Decision Step). There are various possibilities how these two basic steps can be realized, as can be seen in the different proposed detection methods in the literature. An ideal detection algorithm should provide high recognition sensitiv ity with high decision accuracy and require a reasonable computation effort . In reality, a gain in sensitivity is usually only possible with a loss in decision accuracy and with a higher computational effort. So, automatic detection of faint streaks is still a challenge. This paper presents a detection algorithm using spatial filters simulating the geometrical form of possible streaks on a CCD image. This is realized by image convolution. The goal of this method is to generate a more or less perfect match between a streak and a filter by varying the length and orientation of the filters. The convolution answers are accepted or rejected according to an overall threshold given by the ackground statistics. This approach yields as a first result a huge amount of accepted answers due to filters partially covering streaks or remaining stars. To avoid this, a set of additional acceptance criteria has been included in the detection method. All criteria parameters are justified by background and streak statistics and they affect the detection sensitivity only marginally. Tests on images containing simulated streaks and on real images containing satellite streaks show a very promising sensitivity, reliability and running speed for this detection method. Since all method parameters are based on statistics, the true alarm, as well as the false alarm probability, are well controllable. Moreover, the proposed method does not pose any extraordinary demands on the computer hardware and on the image acquisition process.

Detection of Malnutrition in Patients Undergoing Maintenance Haemodialysis: A Quantitative Data Analysis on 12 Parameters

Background Protein-energy-malnutrition (PEM) is common in people with end stage kidney disease (ESKD) undergoing maintenance haemodialysis (MHD) and correlates strongly with mortality. To this day, there is no gold standard for detecting PEM in patients on MHD. Aim of Study The aim of this study was to evaluate if Nutritional Risk Screening 2002 (NRS-2002), handgrip strength measurement, mid-upper arm muscle area (MUAMA), triceps skin fold measurement (TSF), serum albumin, normalised protein catabolic rate (nPCR), Kt/V and eKt/V, dry body weight, body mass index (BMI), age and time since start on MHD are relevant for assessing PEM in patients on MHD. Methods The predictive value of the selected parameters on mortality and mortality or weight loss of more than 5% was assessed. Quantitative data analysis of the 12 parameters in the same patients on MHD in autumn 2009 (n = 64) and spring 2011 (n = 40) with paired statistical analysis and multivariate logistic regression analysis was performed. Results Paired data analysis showed significant reduction of dry body weight, BMI and nPCR. Kt/Vtot did not change, eKt/v and hand grip strength measurements were significantly higher in spring 2011. No changes were detected in TSF, serum albumin, NRS-2002 and MUAMA. Serum albumin was shown to be the only predictor of death and of the combined endpoint “death or weight loss of more than 5%”. Conclusion We now screen patients biannually for serum albumin, nPCR, Kt/V, handgrip measurement of the shunt-free arm, dry body weight, age and time since initiation of MHD.

Detection of haemorrhagic shock: Are vital signs obsolete? A commentary by Dr M. Luginbühl

The ATLS program by the American college of surgeons is probably the most important globally active training organization dedicated to improve trauma management. Detection of acute haemorrhagic shock belongs to the key issues in clinical practice and thus also in medical teaching. (In this issue of the journal William Schulz and Ian McConachrie critically review the ATLS shock classification Table 1), which has been criticized after several attempts of validation have failed [1]. The main problem is that distinct ranges of heart rate are related to ranges of uncompensated blood loss and that the heart rate decrease observed in severe haemorrhagic shock is ignored [2]. Table 1. Estimated blood loos based on patient's initial presentation (ATLS Students Course Manual, 9th Edition, American College of Surgeons 2012). Class I Class II Class III Class IV Blood loss ml Up to 750 750–1500 1500–2000 >2000 Blood loss (% blood volume) Up to 15% 15–30% 30–40% >40% Pulse rate (BPM) <100 100–120 120–140 >140 Systolic blood pressure Normal Normal Decreased Decreased Pulse pressure Normal or ↑ Decreased Decreased Decreased Respiratory rate 14–20 20–30 30–40 >35 Urine output (ml/h) >30 20–30 5–15 negligible CNS/mental status Slightly anxious Mildly anxious Anxious, confused Confused, lethargic Initial fluid replacement Crystalloid Crystalloid Crystalloid and blood Crystalloid and blood Table options In a retrospective evaluation of the Trauma Audit and Research Network (TARN) database blood loss was estimated according to the injuries in nearly 165,000 adult trauma patients and each patient was allocated to one of the four ATLS shock classes [3]. Although heart rate increased and systolic blood pressure decreased from class I to class IV, respiratory rate and GCS were similar. The median heart rate in class IV patients was substantially lower than the value of 140 min−1 postulated by ATLS. Moreover deterioration of the different parameters does not necessarily go parallel as suggested in the ATLS shock classification [4] and [5]. In all these studies injury severity score (ISS) and mortality increased with in increasing shock class [3] and with increasing heart rate and decreasing blood pressure [4] and [5]. This supports the general concept that the higher heart rate and the lower blood pressure, the sicker is the patient. A prospective study attempted to validate a shock classification derived from the ATLS shock classes [6]. The authors used a combination of heart rate, blood pressure, clinically estimated blood loss and response to fluid resuscitation to classify trauma patients (Table 2) [6]. In their initial assessment of 715 predominantly blunt trauma patients 78% were classified as normal (Class 0), 14% as Class I, 6% as Class II and only 1% as Class III and Class IV respectively. This corresponds to the results from the previous retrospective studies [4] and [5]. The main endpoint used in the prospective study was therefore presence or absence of significant haemorrhage, defined as chest tube drainage >500 ml, evidence of >500 ml of blood loss in peritoneum, retroperitoneum or pelvic cavity on CT scan or requirement of any blood transfusion >2000 ml of crystalloid. Because of the low prevalence of class II or higher grades statistical evaluation was limited to a comparison between Class 0 and Class I–IV combined. As in the retrospective studies, Lawton did not find a statistical difference of heart rate and blood pressure among the five groups either, although there was a tendency to a higher heart rate in Class II patients. Apparently classification during primary survey did not rely on vital signs but considered the rather soft criterion of “clinical estimation of blood loss” and requirement of fluid substitution. This suggests that allocation of an individual patient to a shock classification was probably more an intuitive decision than an objective calculation the shock classification. Nevertheless it was a significant predictor of ISS [6]. Table 2. Shock grade categories in prospective validation study (Lawton, 2014) [6]. Normal No haemorrhage Class I Mild Class II Moderate Class III Severe Class IV Moribund Vitals Normal Normal HR > 100 with SBP >90 mmHg SBP < 90 mmHg SBP < 90 mmHg or imminent arrest Response to fluid bolus (1000 ml) NA Yes, no further fluid required Yes, no further fluid required Requires repeated fluid boluses Declining SBP despite fluid boluses Estimated blood loss (ml) None Up to 750 750–1500 1500–2000 >2000 Table options What does this mean for clinical practice and medical teaching? All these studies illustrate the difficulty to validate a useful and accepted physiologic general concept of the response of the organism to fluid loss: Decrease of cardiac output, increase of heart rate, decrease of pulse pressure occurring first and hypotension and bradycardia occurring only later. Increasing heart rate, increasing diastolic blood pressure or decreasing systolic blood pressure should make any clinician consider hypovolaemia first, because it is treatable and deterioration of the patient is preventable. This is true for the patient on the ward, the sedated patient in the intensive care unit or the anesthetized patients in the OR. We will therefore continue to teach this typical pattern but will continue to mention the exceptions and pitfalls on a second stage. The shock classification of ATLS is primarily used to illustrate the typical pattern of acute haemorrhagic shock (tachycardia and hypotension) as opposed to the Cushing reflex (bradycardia and hypertension) in severe head injury and intracranial hypertension or to the neurogenic shock in acute tetraplegia or high paraplegia (relative bradycardia and hypotension). Schulz and McConachrie nicely summarize the various confounders and exceptions from the general pattern and explain why in clinical reality patients often do not present with the “typical” pictures of our textbooks [1]. ATLS refers to the pitfalls in the signs of acute haemorrhage as well: Advanced age, athletes, pregnancy, medications and pace makers and explicitly state that individual subjects may not follow the general pattern. Obviously the ATLS shock classification which is the basis for a number of questions in the written test of the ATLS students course and which has been used for decades probably needs modification and cannot be literally applied in clinical practice. The European Trauma Course, another important Trauma training program uses the same parameters to estimate blood loss together with clinical exam and laboratory findings (e.g. base deficit and lactate) but does not use a shock classification related to absolute values. In conclusion the typical physiologic response to haemorrhage as illustrated by the ATLS shock classes remains an important issue in clinical practice and in teaching. The estimation of the severity haemorrhage in the initial assessment trauma patients is (and was never) solely based on vital signs only but includes the pattern of injuries, the requirement of fluid substitution and potential confounders. Vital signs are not obsolete especially in the course of treatment but must be interpreted in view of the clinical context. Conflict of interest None declared. Member of Swiss national ATLS core faculty.

A simulation study on the statistical monitoring of condemnation rates from slaughterhouses for syndromic surveillance: an evaluation based on Swiss data

Syndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (e.g. economic). Several European SyS systems use data collected during meat inspection for syndromic surveillance of animal health, as some diseases may be more easily detected post-mortem than at their point of origin or during the ante-mortem inspection upon arrival at the slaughterhouse. In this paper we use simulation to evaluate the performance of a quasi-Poisson regression (also known as an improved Farrington) algorithm for the detection of disease outbreaks during post-mortem inspection of slaughtered animals. When parameterizing the algorithm based on the retrospective analyses of 6 years of historic data, the probability of detection was satisfactory for large (range 83-445 cases) outbreaks but poor for small (range 20-177 cases) outbreaks. Varying the amount of historical data used to fit the algorithm can help increasing the probability of detection for small outbreaks. However, while the use of a 0·975 quantile generated a low false-positive rate, in most cases, more than 50% of outbreak cases had already occurred at the time of detection. High variance observed in the whole carcass condemnations time-series, and lack of flexibility in terms of the temporal distribution of simulated outbreaks resulting from low reporting frequency (monthly), constitute major challenges for early detection of outbreaks in the livestock population based on meat inspection data. Reporting frequency should be increased in the future to improve timeliness of the SyS system while increased sensitivity may be achieved by integrating meat inspection data into a multivariate system simultaneously evaluating multiple sources of data on livestock health.

THE RELATIONSHIP BETWEEN SERUM CHOLESTEROL AND CANCER INCIDENCE IN THE POPULATION OF THE HYPERTENSION DETECTION AND FOLLOW-UP PROGRAM

The relationship between serum cholesterol and cancer incidence was investigated in the population of the Hypertension Detection and Follow-up Program (HDFP). The HDFP was a multi-center trial designed to test the effectiveness of a stepped program of medication in reducing mortality associated with hypertension. Over 10,000 participants, ages 30-69, were followed with clinic and home visits for a minimum of five years. Cancer incidence was ascertained from existing study documents, which included hospitalization records, autopsy reports and death certificates. During the five years of follow-up, 286 new cancer cases were documented. The distribution of sites and total number of cases were similar to those predicted using rates from the Third National Cancer Survey. A non-fasting baseline serum cholesterol level was available for most participants. Age, sex, and race specific five-year cancer incidence rates were computed for each cholesterol quartile. Rates were also computed by smoking status, education status, and percent ideal weight quartiles. In addition, these and other factors were investigated with the use of the multiple logistic model.^ For all cancers combined, a significant inverse relationship existed between baseline serum cholesterol levels and cancer incidence. Previously documented associations between smoking, education and cancer were also demonstrated but did not account for the relationship between serum cholesterol and cancer. The relationship was more evident in males than females but this was felt to represent the different distribution of occurrence of specific cancer sites in the two sexes. The inverse relationship existed for all specific sites investigated (except breast) although a level of statistical significance was reached only for prostate carcinoma. Analyses after exclusion of cases diagnosed during the first two years of follow-up still yielded an inverse relationship. Life table analysis indicated that competing risks during the period of follow-up did not account for the existence of an inverse relationship. It is concluded that a weak inverse relationship does exist between serum cholesterol for many but not all cancer sites. This relationship is not due to confounding by other known cancer risk factors, competing risks or persons entering the study with undiagnosed cancer. Not enough information is available at the present time to determine whether this relationship is causal and further research is suggested. ^

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^

Benford's Law and Fraud Detection. Facts and Legends

Is Benford's law a good instrument to detect fraud in reports of statistical and scientific data? For a valid test the probability of "false positives" and "false negatives" has to be low. However, it is very doubtful whether the Benford distribution is an appropriate tool to discriminate between manipulated and non-manipulated estimates. Further research should focus more on the validity of the test and test results should be interpreted more carefully.

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth?s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech

We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech.